Improving the precision of QT measurements

Background: Accurate and precise QT interval measurement is very important for both regulatory and drug developmental decision making. These measurements are often made using a manual or semi-automated technique, and the associated variability necessitates sample sizes of around 50 to 70 subjects in thorough QT/QTc studies. The purpose of this study was to compare the reproducibility and precision of a semi-automated (SA) method and a high-precision (HPQT) technique for ECG extraction and QT interval measurement on two thorough QT/QTc (TQT) studies conducted in compliance with ICH E14. Methods: Data from 35 healthy subjects from two different crossover TQT studies on treatment with placebo and moxifloxacin was analyzed. Both methods examined the RR and QT intervals measured in lead II or the lead with the highest quality T-wave on a single beat basis using the QT algorithm included in the COMPAS software package. ECGs were measured at a protocol-specific timepoint. Results: The effect of moxifloxacin on the QTc interval was highly reproducible in the two studies, and assay sensitivity was met with both methods. Pairwise comparison of QTcF values between methods demonstrated high agreement with no bias, small mean differences (below 1.5 ms) and narrow limits of agreement. HPQT improved the precision of the QTc measurement by 31% in Study I (standard deviation of DQTcF: SA 8.9 ms; HPQT 6.3 ms) and by 15% in Study II (SD: SA 9.7 ms; HPQT 8.3 ms). Conclusions: The HPQT QT measurement technique detected the effect induced by moxifloxacin with the same accuracy as SA techniques, and with clearly improved precision. More precise QTc measurement has important implications in terms of lowering the likelihood of false positive results and/or reducing the sample size in TQT studies, as well as improving the utility of QT assessment in early clinical development. (Cardiol J 2011; 18, 4: 401–410

When Tutors Self Tutor: What Preservice Teachers Learn about Themselves, Technology, and Teaching during a Time of Remote Learning

Tutoring has been called “the act of saying to somebody else, ‘Let me help you learn’ ” (Chin, Rabow, & Estrada, 2011, p. 2). Teacher preparation programs regularly encourage college students becoming elementary or secondary school teachers to tutor younger learners as an early school-based field experience. Tutors, acting as teachers, must learn how to provide ideas and information to aid and encourage students to accomplish learning goals. Tutors must also assess the effectiveness of their decisions about when and how to implement different instructional strategies to respond to students’ needs

Estilos de Vida, Medio Ambiente y Salud - ME151 - 202102

Estilos de Vida, Medio Ambiente y Salud es un curso que desarrolla en los estudiantes de Ciencias de la Salud la capacidad de valorar la convivencia humana en sociedades plurales teniendo en cuenta los aspectos éticos y morales de las acciones y decisiones que se toman además de las consecuencias de las mismas en el marco de respeto de los deberes y derechos ciudadanos, así como la capacidad de detectar las oportunidades para generar propuestas innovadoras en base a una planificación eficiente. Para ello el estudiante comprende la interacción de los Estilos de Vida y el Ambiente como factores determinantes de la salud de individuos y poblaciones. El curso de Estilos de Vida, Ambiente y Salud busca desarrollar la competencia general de Ciudadanía y Pensamiento Innovador Nivel 1 y además la competencia especifica de Práctica de Salud Pública, donde el estudiante describe la interacción entre ambiente, estilos de vida, la participación social y su relación con la salud de los individuos y de las poblaciones a nivel nacional y global. El curso está dirigido a estudiantes de todas las carreras de Ciencias de la Salud como Medicina Humana, Veterinaria y Odontología del 5to ciclo, Nutrición 4to ciclo, Terapia Física y Psicología 2do ciclo. Es un curso eminentemente práctico y enfocado en dar a los estudiantes las herramientas necesarias para la comprensión de los diferentes factores que influyen en la salud, las habilidades para valorar la convivencia del hombre y su entorno, así como detectar necesidades y oportunidades para proyectos y propuestas en base a una adecuada 1planificación y toma de decisiones eficientes que le permitirán desarrollarse y desenvolverse en su desarrollo profesional

Polygenic risk scores for prediction of breast cancer and breast cancer subtypes

Abstract Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57–1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628–0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs